Improving Transcatheter Aortic Valve Implantation Efficiency and Outcomes in the Canadian Healthcare Environment
Bibliographic record
Abstract
Background With increased demand for transcatheter aortic valve implantation (TAVI), comprehensive strategies are required to improve procedural efficiency and increase case loads at Canadian TAVI programs. Methods Time efficiency measures were implemented at the pre-procedure, anesthesia, procedural, and post-procedural stages at our centre between 2019 to 2024 using a one day per week strategy in a hybrid operating room. We analyzed trends in case volumes and procedural times during the study period. Patients were stratified based on whether their procedure was performed on high case volume (HCV) versus low case volume (LCV) days and differences in early clinical outcomes were analyzed. Effect of specific efficiency measures on procedural times were also analyzed. Results Total of 1019 patients were analyzed. We noted significant increases in TAVI volumes (+0.51 cases/year, p<0.0001) and decrease in total operating room time (-10.2 min/year, p<0.0001) during the study period. The time savings were driven most strongly be decreases in anesthesia and procedure times. There were no significant differences between HCV and LCV groups in terms of in-hospital mortality (0.7% vs 1.3%, p=0.20), stroke/TIA (2.4% vs 2.4%, p=0.25), new pacemaker (9.0% vs 10.1%, p=0.64), moderate-severe paravalvular leak (2.9% vs 5.2%, p=0.25), but there was increased bleeding in the LCV group (3.9% vs. 2.6%, p=0.028). Multivariable linear regression showed that use of conscious sedation, left ventricular pacing and hybrid closure strategies significantly reduced total TAVI times. Conclusions Our comprehensive, innovative time efficiency strategies have improved TAVI efficiency significantly while preserving good outcomes and not requiring significant additional resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".